Spatiotemporal requirements of the Hainan gibbon: Does home range constrain recovery of the world's rarest ape?

Bryant, Jessica V.; Zeng, Xingyuan; Hong, Xin; Chatterjee, Helen J.; Turvey, Samuel T.

doi:10.1002/ajp.22617

Cited by 18 publications

(19 citation statements)

References 51 publications

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“…We manually labelled 32 8-h recordings by inspecting spectrograms and listening to audio using Sonic Visualiser (Cannam et al, 2010), and end times, and the number of notes, of each observed gibbon phrase. Four files containing no gibbon calls were discarded, as periods without gibbon calls are readily available from the remaining 28 files.…”

Section: Discussionmentioning

confidence: 99%

Automated detection of Hainan gibbon calls for passive acoustic monitoring

Dufourq

Durbach

Hansford

et al. 2021

Remote Sens Ecol Conserv

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Extracting species calls from passive acoustic recordings is a common preliminary step to ecological analysis. For many species, particularly those occupying noisy, acoustically variable habitats, the call extraction process continues to be largely manual, a time-consuming and increasingly unsustainable process. Deep neural networks have been shown to offer excellent performance across a range of acoustic classification applications, but are relatively underused in ecology. We describe the steps involved in developing an automated classifier for a passive acoustic monitoring project, using the identification of calls of the Hainan gibbon Nomascus hainanus, one of the world's rarest mammal species, as a case study. This includes preprocessing-selecting a temporal resolution, windowing and annotation; data augmentation; processing-choosing and fitting appropriate neural network models; and post-processing-linking model predictions to replace, or more likely facilitate, manual labelling. Our best model converted acoustic recordings into spectrogram images on the mel frequency scale, using these to train a convolutional neural network. Model predictions were highly accurate, with per-second false positive and false negative rates of 1.5% and 22.3%. Nearly all false negatives were at the fringes of calls, adjacent to segments where the call was correctly identified, so that very few calls were missed altogether. A post-processing step identifying intervals of repeated calling reduced an 8-h recording to, on average, 22 min for manual processing, and did not miss any calling bouts over 72 h of test recordings. Gibbon calling bouts were detected regularly in multi-month recordings from all selected survey points within Bawangling National Nature Reserve, Hainan. We demonstrate that passive acoustic monitoring incorporating an automated classifier represents an effective tool for remote detection of one of the world's rarest and most threatened species. Our study highlights the viability of using neural networks to automate or greatly assist the manual labelling of data collected by passive acoustic monitoring projects. We emphasize that model development and implementation be informed and guided by ecological objectives, and increase accessibility of these tools with a series of notebooks that allow users to build and deploy their own acoustic classifiers.

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Section: Discussionmentioning

confidence: 99%

Automated detection of Hainan gibbon calls for passive acoustic monitoring

Dufourq

Durbach

Hansford

et al. 2021

Remote Sens Ecol Conserv

Self Cite

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“…It is uncertain whether within-recorder and between-recorder variation in calling bout detections represents variation in calling frequency between groups, and/or variation in detection effectiveness by recorders, with the latter possibility likely associated with specific recorder placement, local terrain, specific gibbon movement patterns across landscapes, and group home range size (cf. Bryant et al (2017)). Future work could investigate detection likelihood in relation to specific environmental parameters and local weather conditions (e.g., rainfall, wind, temperature), data on which were not available for our survey period but are known to affect calling behaviour in other gibbons (Coudrat, Nanthavong, Ngoprasert, Suwanwaree, & Savini, 2015;Yin et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Recorders were attached to trees at a height of approximately 1.5m in tropical evergreen forest. Four recorders were situated within the known home ranges of the four Hainan gibbon social groups existing during the study period (Groups A-D; see Bryant, Zeng, Hong, Chatterjee, and Turvey (2017)), three were situated at locations intermediate between known home ranges, and a further recorder was placed in an area where a solitary male gibbon was thought to occur (Bryant et al, 2016). They were placed at locations that were used as regular listening posts for monitoring gibbons by reserve staff ( Figure 1).…”

Section: Data Collectionmentioning

confidence: 99%

Automated detection of Hainan gibbon calls for passive acoustic monitoring

Dufourq

Durbach

Hansford

et al. 2020

Preprint

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1AbstractExtracting species calls from passive acoustic recordings is a common preliminary step to ecological analysis. For many species, particularly those occupying noisy, acoustically variable habitats, the call extraction process continues to be largely manual, a time-consuming and increasingly unsustainable process. Deep neural networks have been shown to offer excellent performance across a range of acoustic classification applications, but are relatively underused in ecology.We describe the steps involved in developing an automated classifier for a passive acoustic monitoring project, using the identification of calls of the Hainan gibbon (Nomascus hainanus), one of the world’s rarest mammal species, as a case study. This includes preprocessing - selecting a temporal resolution, windowing and annotation; data augmentation; processing - choosing and fitting appropriate neural network models; and postprocessing - linking model predictions to replace, or more likely facilitate, manual labelling.Our best model converted acoustic recordings into spectrogram images on the mel frequency scale, using these to train a convolutional neural network. Model predictions were highly accurate, with per-second false positive and false negative rates of 1.5% and 22.3%. Nearly all false negatives were at the fringes of calls, adjacent to segments where the call was correctly identified, so that very few calls were missed altogether. A postprocessing step identifying intervals of repeated calling reduced an eight-hour recording to, on average, 22 minutes for manual processing, and did not miss any calling bouts over 72 hours of test recordings. Gibbon calling bouts were detected regularly in multi-month recordings from all selected survey points within Bawangling National Nature Reserve, Hainan.We demonstrate that passive acoustic monitoring incorporating an automated classifier represents an effective tool for remote detection of one of the world’s rarest and most threatened species. Our study highlights the viability of using neural networks to automate or greatly assist the manual labelling of data collected by passive acoustic monitoring projects. We emphasise that model development and implementation be informed and guided by ecological objectives, and increase accessibility of these tools with a series of notebooks that allow users to build and deploy their own acoustic classifiers.

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“…Accordingly, future priority actions in BNNR have to be aimed at (1) further protecting large food trees and habitat-exclusive food species in each habitat; (2) promoting recovery and regeneration of natural forest fragments and plantations where most of the important food tree species are associated; (3) implementing special measures (such as directional felling) to avoid damaging food trees and reducing the degree of forest canopy damage so as to preserve important food resources and nesting sites for Hainan gibbons and (4) extending our research to predict the potential distributions of important food tree species and analysing the effect of food availability on Hainan gibbon. Of courses, besides food availability, other factors, such as genetic and anthropogenic factors, are also likely to constrain Hainan gibbon population (Bryant et al 2016;Bryant et al 2017) and conservation actions should also focus on managing these factors. If these measures are adopted in the near future, there is a realistic chance that this rare species can survive and remain amongst those species that are characteristic to Hainan Island.…”

Section: Implications For Conservationmentioning

confidence: 99%

Diversity and distribution of food plants: Implications for conservation of the critically endangered Hainan gibbon

Zhang¹,

Zang²

2018

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An understanding of the diversity and distribution patterns of Hainan gibbon (Nomascushainanus) foods is essential to its conservation. We used data from plots in various successional stages and Pinusmerkusii plantations (PF) of Bawangling National Nature Reserve (BNNR) to compare variations in food species diversity and composition amongst forest types. A total of 85 food species and 16,882 food plants individuals were found across forest types. Habitat-exclusive food species were most abundant in old growth natural forest (OGF), followed by mid-aged natural secondary forest (MSF). We did not find exclusive species in PF. For all food species, as well as each stem size class, PF displayed a lower species richness and abundance and, in addition, less similar species composition in each age class compared to secondary forests. The highest stem density and species richness were found in MSF. The abundance of food trees was higher in MSF and OGF than in young natural secondary forest. Results suggested that MSF could serve as an alternative habitat for Hainan gibbons after short-term recovery. Hainan gibbons might be limited to secondary forests older than 25 years old. PF was found to be unsuitable for Hainan gibbons.

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Spatiotemporal requirements of the Hainan gibbon: Does home range constrain recovery of the world's rarest ape?

Abstract: Re-evaluation reveals Hainan gibbon home range as c. 1-2 km . Hainan gibbon home range is, therefore, similar to other Nomascus gibbons. Limited data for extremely rare species does not necessarily prevent derivation of robust home range estimates.

Cited by 18 publications

References 51 publications

Automated detection of Hainan gibbon calls for passive acoustic monitoring

Automated detection of Hainan gibbon calls for passive acoustic monitoring

Automated detection of Hainan gibbon calls for passive acoustic monitoring

Diversity and distribution of food plants: Implications for conservation of the critically endangered Hainan gibbon

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